11–20 of 81 posts
This is massively ground breaking. You'll get it if you've used motion tracking on several game interfaces and had to make perfectly white backgrounds with bright lights to make it work. This is incredibly accurate - really game changing stuff.
With something like this we could have truly “Minority Report” style human-computer interface. Actually, the guy who invented the Minority Report interface commercialized it and has been selling it for years. Product website: http://oblong.com Edit better video: http://www.ted.com/talks/john_underkoffler_drive_3d_data_wit...
But facial recognition aside, the uses are endless. If it can be brought to the same level Kinect drivers are at, but with finger tracking and no custom hardware, this could change everything.
This is massively ground breaking. You'll get it if you've used motion tracking on several game interfaces and had to make perfectly white backgrounds with bright lights to make it work. This is incredibly accurate - really game changing stuff.
Sounds like you're used to bad algorithms. I think there is a serious disconnect between the state of the art in computer vision and what's used in industry.
The demo was cool, but the techniques are not that revolutionary. From a cursory glance through the papers, it is basically AdaBoost (for detection) and Lucas-Kanade (for tracking), with a few extensions.
Not to discount the guy's work at all, it's very cool and does a good job of pulling together existing algorithms. But not groundbreaking in the sense of, say, Viola-Jones was for object detection.
> Can Predator be used to stabilize and navigate a Quadcopter? > That is not straightforward. anyone know why not?
So the question is, can predator be used to improve mapping? AFAIK, that would require a) automatically selecting trackable objects and b) tracking many of them simultaneously. That PTAM technique tracks thousands, but with tracking this reliable, you might get by with much less.
So, more work is required to apply it to mapping, but I have to imagine it could be done. And seeing how well predator adapts to changes in scale, orientation, and visibility, I suspect it could improve mapping considerably.
Clearly it could be applied immediately to robotic manufacturing. Tracking parts, understanding their orientation, and manipulating them all get easier when its 'cheap' to add additional tracking sensors.
Three systems sharing data (front, side, top) would give some very good expressive options for motion based UIs or control.
Depending on how well the computational load can be reduced to hardware small systems could provide for head mounted tracking systems. (see CMUCam [1] for small)
The training aspect seems to be a weak link, in that some applications would need to have the camera 'discover' what to track and then track it.
A number of very expensive object tracking systems used by law enforcement and the military might get a bit cheaper.
Photographers might get a mode where they can specify 'take the picture when this thing is centered in the frame' for sports and other high speed activities.
Very nice piece of work.
This is massively ground breaking. You'll get it if you've used motion tracking on several game interfaces and had to make perfectly white backgrounds with bright lights to make it work. This is incredibly accurate - really game changing stuff.
>This is massively ground breaking. Sounds like you're used to bad algorithms. I think there is a serious disconnect between the state of the art in computer vision and what's used in industry. The demo was cool, but the techniques are not that revolutionary. From a cursory glance through the papers, it is basically AdaBoost (for detection) and Lucas-Kanade (for tracking), with a few extensions. Not to discount the g…
This is massively ground breaking. You'll get it if you've used motion tracking on several game interfaces and had to make perfectly white backgrounds with bright lights to make it work. This is incredibly accurate - really game changing stuff.
>This is massively ground breaking. Sounds like you're used to bad algorithms. I think there is a serious disconnect between the state of the art in computer vision and what's used in industry. The demo was cool, but the techniques are not that revolutionary. From a cursory glance through the papers, it is basically AdaBoost (for detection) and Lucas-Kanade (for tracking), with a few extensions. Not to discount the g…
There's a lot of current work going on that effectively splits computer vision into multiple parallel tasks for better results but uses previously well known techniques (PTAM is another good example).
As an aside, I read through the paper and it doesn't look like this could track, say, your index finger separately from other fingers if, for a moment, your hand was occluded. This pretty much bars using this exclusively in a Minority Report style interface (you would need hand pose tracking like the stuff Kinect does). Though, I'm just re-iterating your point that this isn't the second coming of computer vision.
That being said, there are some really good ideas here.
Earlier quoted context omitted.
>This is massively ground breaking. Sounds like you're used to bad algorithms. I think there is a serious disconnect between the state of the art in computer vision and what's used in industry. The demo was cool, but the techniques are not that revolutionary. From a cursory glance through the papers, it is basically AdaBoost (for detection) and Lucas-Kanade (for tracking), with a few extensions. Not to discount the g…
I think that it has an online-training algo for their 2-bit binary pattern as well. Haven't checked out the paper yet though.
This is massively ground breaking. You'll get it if you've used motion tracking on several game interfaces and had to make perfectly white backgrounds with bright lights to make it work. This is incredibly accurate - really game changing stuff.